Performance evaluation based on resource dynamics

ABSTRACT

Methods and systems of evaluating a performance of an entity are described. A processor may obtain first data indicating tier attributes of resources of the entity, second data indicating function attributes of the resources, and third data indicating productivity attributes of the resources. The processor may train a model based on the first data, the second data, and the third data, the model may represent transitions of the resources over time. The processor may receive a set of controls including at least an objective to optimize a performance of the entity. The processor may generate a controlled model by integrating the set of controls into the model. The processor may determine a set of outcomes from the controlled model that includes at least a set of transitions relating to the resources that may optimize the performance of the entity.

BACKGROUND

The present application relates generally to computers, and computerapplications, and more particularly to computer-implemented methods andsystems relating to resource management systems.

An entity (e.g., an organization, a company) may include a plurality ofresources such as computer resources, equipments, a workforce, and/orother types of resources. In some examples, the entity may experiencemovements of resources, such as when resources are added, or movingbetween different tiers (e.g., tier promotions, switch of positions). Insome examples, the tiers of resources are defined by multi-dimensionaland oftentimes high-dimensional attributes, such as, a software engineerof a particular tier may be an expert in a first function (e.g., aprogramming language) and may be proficient in a second function. Themovements of the resources between different tiers, and also theprogression and regression of attributes such as a competency possessedby a resource in a function or skill, are transitions that constituteresource dynamics within the entity. These resource dynamics maysometimes include unpredictable behavioral characteristics and mayinclude uncertainties that will affect an overall performance of theentity.

SUMMARY

In some examples, a method of evaluating a performance of an entity isgenerally described. The method may include obtaining, by a processor,first data indicating tier attributes of a plurality of resources of anentity. The method may further include obtaining, by the processor,second data indicating function attributes of the plurality ofresources. The method may further include obtaining, by the processor,third data indicating productivity attributes of the plurality ofresources. The method may further include training, by the processor, amodel based on the first data, the second data, and the third data. Themodel may represent one or more transitions of the plurality ofresources over time. The transitions of the plurality of resources maybe based on the tier attribute, the function attribute, and theproductivity attribute of the plurality of resources. The method mayfurther include receiving, by the processor, a set of controlscomprising at least an objective to optimize a performance of theentity. The method may further include generating, by the processor, acontrolled model by integrating the set of controls into the model. Themethod may further include determining, by the processor, a set ofoutcomes from the controlled model. The set of outcomes may include atleast a set of transitions relating to the plurality of resources, andthe set of transitions may optimize the performance of the entity.

In some examples, a system of evaluating a performance of an entity isgenerally described. The system may include a memory device and ahardware processor configured to be in communication with each other.The memory device may be configured to store a database. The databasemay include first data indicating tier attributes of a plurality ofresources of an entity, second data indicating function attributes ofthe plurality of resources, and third data indicating productivityattributes of the plurality of resources. The hardware processor may beconfigured to obtain the first data, the second data, and the third datafrom the memory device. The hardware processor may be further configuredto train a model based on the first data, the second data, and the thirddata. The model may represent one or more transitions of the pluralityof resources over time. The transitions of the plurality of resourcesmay be based on the tier attribute, the function attribute, and theproductivity attribute of the plurality of resources. The hardwareprocessor may be further configured to receive a set of controlscomprising at least an objective to optimize a performance of theentity. The hardware processor may be further configured to generate acontrolled model by integrating the set of controls into the model. Thehardware processor may be further configured to determine a set ofoutcomes from the controlled model. The set of outcomes may include atleast a set of transitions relating to the plurality of resources, andthe set of transitions may optimize the performance of the entity.

In some examples, a computer program product of evaluating a performanceof an entity is generally described. The computer program product mayinclude a computer readable storage medium having program instructionsembodied therewith. The program instructions may be executable by aprocessing element of a device to cause the device to perform one ormore methods described herein.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example computer system, in one embodiment, thatcan be utilized to implement performance evaluation based on resourcedynamics.

FIG. 2 illustrates a flow diagram relating to a process, in oneembodiment, to implement performance evaluation based on resourcedynamics.

FIG. 3 illustrates a schematic of an example computer or processingsystem that may implement performance evaluation based on resourcedynamics in one embodiment.

FIG. 4 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 5 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

In some examples, the entity may operate under different compositions,or combinations, of resources at different situations. For example, afirst composition of resources may be assigned to a first task, while asecond composition of resources may be assigned to a second task.Different compositions assigned to the same task may result in differentoutcomes of the task, and may affect the performance of the entitydifferently. In some examples, the outcomes from each composition ofresources may be evaluated, such as by using a performance evaluationsystem configured to evaluate an effect of the outcomes on theperformance of the entity (e.g., completeness, efficiency, profit). Forexample, the performance evaluation system may evaluate an outcome(e.g., profits) from a sales team in the past three months and analyzehow the outcomes affect the performance of the entity. However, suchanalysis may be performed with coarse data granularity, and theevaluations may be based on discrete-time analysis, which neglects thedynamics of the uncertainties within the intervals between each timeinstance. For example, the movement of the sales team, such as personnelchange dynamics in the sales team, or whether particular personnelattended training programs within the three months to improve particularskills, are not part of the evaluation. In order to account for theseneglected uncertainties, there is a need for an improved performanceevaluation system to evaluate performances of resources and entity whileconsidering the resource dynamics of the entity, and also addressingcomplex, behavior-related characteristics and uncertainties associatedwith the resource dynamics. Specifically, there is a need to improve theperformance evaluation system to take into account the utilities ramp-uptime after each resource dynamics event (e.g., changes to the resourcesof the entity), or the eligibility of individual resources before eachresource dynamics event. There is also a need to improve the performanceevaluation system by modeling and optimizing the resource dynamics inthe entity to improve both individual utility (bottom up) and attainmentof target outcomes (top down).

To be described in more detail below, a system 100 in accordance withthe present disclosure is an improved performance evaluation system thatmodels resource dynamics including post-action utilities ramp-up timeand evolution, as well as pre-action eligibility measures using, forexample, measure-valued processes. The system 100 further controls theresulting measure-valued processes from both top-down and bottom-up andperforms optimizations to reduce ramp-up time or to improve eligibilitymeasures. The system 100 also models uncertainties and the resourcedynamics using multi-time-scale stochastic processes, for example,discrete-time Markov chains with Brownian bridge embedded betweendiscrete time epochs and provides optimal control of the resultingstochastic processes. Thus, the system 100 is configured to manage theresource dynamics of the entity by addressing complex,human-behavior-related characteristics and uncertainties that may havebeen neglected in traditional performance evaluation systems.

FIG. 1 illustrates an example computer system, in one embodiment, thatcan be utilized to implement performance evaluation based on resourcedynamics, arranged in accordance with at least some embodimentsdescribed herein. In some examples, the system 100 may be implementedwith a processor 120 and a memory 122 configured to be in communicationwith each other. In some examples, the processor 120 may be a centralprocessing unit of a computer device, and may be configured to controloperations of the memory 122 and/or other components of the computerdevice. In some examples, the system 100 may include additional hardwarecomponents, such as programmable logic devices, microcontrollers, memorydevices, and/or other hardware components, that may be configured toperform respective tasks of the methods described in the presentdisclosure. In some examples, the processor 120 may be configured toexecute software modules that include instructions to perform eachrespective task of the methods described in the present disclosure. Insome examples, the processor 120 and the memory 122 may be components ofa cloud computing platform that may be employed (such as by an entity130) to implement the methods described in accordance with the presentdisclosure.

The memory 122 is configured to selectively store instructionsexecutable by the processor 120. For example, in one embodiment, thememory 122 may store a set of evaluation instructions 124 (“instructions124”), where the evaluation instructions 124 include instructions, suchas executable code, related to machine learning algorithms and/or otheralgorithms or techniques, which may implement the system 100. Theprocessor 120 is configured to execute one or more portions of theinstructions 124 in order to facilitate implementation of the system100. For example, to be described in more detail below, the processor120 is configured to execute the instructions 124 to train a model 170,impose or integrate controls on the model 170 to generate a controlledmodel 174, determine a set of outcomes 175 by determining a solution tothe controlled model 174, autonomously retrain the model 170 with theoutcomes 175, and output recommendations to compose or allocate one ormore resource of an entity 130 based on the outcomes 175. In someexamples, the instructions 124 may be packaged as a standaloneapplication that may be installed on the computer device implementingthe system 100, such that the instructions 124 may be executed by theprocessor 120 to implement the system 100. In some examples, theinstructions 124 may be stored in a programmable hardware component thatmay be embedded as part of the processor 120.

In an example, the entity 130 may be a company, an organization, and/orother types of entities. The entity 130 may include one or more types ofresources such as computer devices, equipments, a workforce, and/orother resources. In the example shown in FIG. 1, the entity 130 mayinclude a workforce labeled as resources 131, 132, 133, and may includea set of computer devices including resources 151, 152, 153. The memory122 may be further configured to store a resource database 126 thatincludes data identifying the resources of the entity 130. For example,the resource database 126 may include identifiers of each resource 131,132, 133, 151, 152, 153. In some examples, a resource of the entity 130may be assigned, associated, or linked to one or more other resources ofthe entity 130. For example, the resource 151 may be assigned to be usedby the resource 131, the resource 152 may be assigned to be used by theresource 132, the resource 153 may be assigned to be used by theresource 133. In another example, the resources 131 and 132 may belongto a composition 160, and the resources 132 and 133 may belong to acomposition 162. The resource database 126 may further include dataindicating the assignments, links, and associations between theresources of the entity 130.

Further, each resource may correspond to a set of attributes. Forexample, the resource 131 may correspond to attributes 141, the resource132 may correspond to attributes 142, the resource 133 may correspond toattributes 143. Some examples of the attributes 141, 142, 143 mayinclude a tier, one or more skills or function, productivity, and/orother attributes. A tier attribute may be a level, tier, or positionamong a plurality of tiers defined by the entity 130. For example, theentity 130 may define a plurality of tiers for a particular job title(e.g., computer specialist I, computer specialist II, etc.), may defineparticular levels for different job titles and/or experience (e.g.,level-1 for 0-2 years experience, level-2 for 2-4 years experience,etc.), or tiers expressed through texts (e.g., junior computerspecialist, senior computer specialist, etc.). In some examples, a tierof a resource of the entity 130 may reflect other attributes of theresource, such as experience, capabilities, compensation level, rolesophistication, reporting structure, and/or other attributes. Eachresource may transition from one tier to another tier, such as when aparticular resource transition to a higher tier as a result of apromotion or an accumulation of more experience. The tier attribute maychange as resources transition from one tier to another tier. Theprocessor 120 may update the resource database 126 to reflect thetransitions and to update the attributes associated with thetransitions. The transitions of tiers of each resource may constitutethe resource dynamics of the entity 130.

The skills or function attribute may correspond to one or more skills orfunctions possessed by a resource. For example, a programming languagemay be a skill possessed by a resource among the workforce. Each skillor function may be associated with a level, such as novice, proficient,expert, or other indicators of skill levels. Each resource maycorrespond to one or more skills at different skill levels, and acombination of the skills along with corresponding levels in thecombination may reflect other attributes such as an area of expertise,knowledge, talents, abilities, competencies, experience and otherattributes of the corresponding resource. The skills or functionattribute may change, such as increase in competency of a particularskill based on training or additional experience, or regression overtime due to a lack of use or deviation from latest advances in a fieldrelating to the skill. The processor 120 may update the resourcedatabase 126 to reflect the changes to the skills of functions of theresources. The changes in skill or function level are considered astransitions of the function attribute, and may constitute the resourcedynamics of the entity 130.

The productivity attribute may characterize a degree to which resourcescan produce from an operations perspective with respect to thecollection of skills and tiers of the resources, and relative to otherresource with the same skills and tiers. Productivity may be measuredthrough various performance metrics associated with each collection ofskills and tier, such as a completeness of a task performed by theresources, an efficiency of the resources, and/or other performancemetrics. The processor 120 may update the resource database 126 toreflect results of tasks performed by the resources in order todetermine productivity of the resources. The updates and changes toproductivity attribute of the resources may constitute the resourcedynamics of the entity 130.

The processor 120 may receive resource data 102 from various datasources. For example, the resource data 102 may be received from theresource database 126. In some examples, the resource data 102 may bereceived from other databases of the entity 130 that may be stored inthe memory 122, or in memory devices outside of the entity 130 (e.g.,cloud storage). The resource data 102 may include data relating to theresources of the entity 130, including respective tiers, functions, andproductivity attributes of each resource among the resource data 102.

Based on the data reflecting tiers, functions, and productivity ofresources being stored in the resource database 126, the processor 120may quantify the tier, functions, and productivity attributes in orderto train the model 170 with the quantified attributes. In some examples,the processor 120 may quantify the tier, functions, and productivityattributes by assigning values, parameters, variables, functions, and/orother representations to represent each attribute in a format that maybe processed by the processor 120 to train the model 170. The model 170may model various evolutions and transitions within the resourcedynamics of the entity 130 over time. By quantifying the attributes, thesystem 100 may train the model 170 to determine outcomes that willoptimize a performance of the entity 130. Thus, the system 100 is animproved performance evaluation system of the entity 130 by using thequantified tier, function, and productivity attributes to train themodel 170 that models the evolution and dynamics of resource transitionswithin the entity 130. Further, the system 100 may provide statisticalanalysis of data related to the workforce and various decisions orassessments of the entity 130, with a goal of determining, understandingand/or quantifying correlations, causations, relevant effects, andirrelevant effects.

The model 170 may be a time inhomogeneous Markov chain model (furtherdescribed below). In some examples, the model 170 may characterize theevolution of the resources, such as using the transitions between statesof the Markov chain to represent various resource dynamics events overtime, and using the states of the Markov chain to represent theattributes of resources. Some examples of resource dynamics events mayinclude addition or new resources (e.g., new hires), acquisition of newfunctions (e.g., via training), degradation of old functions (e.g., dueto lack of use), promotion or changes to new tiers (e.g., switchpositions or team), and/or other events. Upon training the model 170,the processor 120 may receive a set of controls 172, which may includeone or more constraints and objectives relating to the entity 130. Theprocessor 120 may integrate the controls on the model 170, such as byformulating a stochastic control problem with the constraints and theobjectives, to generate a controlled model 174 that includes the model170 and the controls 172, where the controlled model 174 is a Markovchain. The processor 120 may determine a set of solutions to thecontrolled model 174, where the set of solutions are outcomes 175. Theoutcomes 175 may include data that indicates optimization of aperformance of the entity 130, such as maximizing a profit, optimizingan efficiency, minimizing costs, and/or other performance metrics of theentity 130. For example, the outcomes 175 may indicate a set ofcompositions 160, 162, where each composition includes a respective setof resources, and the distribution of resources in the compositions 160,162 may minimize a cost incurred on the entity 130. In some examples,the processor 120 may evaluate the performance of the entity 130 bydetermining functionals of the controlled model 174. In some examples,the set of outcomes 175 may further include one or more recommendationsand/or instructions, which may modify the one or more resources amongthe entity 130. For example, the set of outcomes 175 may providerecommendations to add new resources to the compositions, or toredistribute resources among the compositions, or to reassign tasks todifferent resources of particular attributes and associated levels, thatmay result in optimizing the performance of the entity 130.

In an example, the system 100 may develop the model 170 usingmicroscopic models that represent the bottom-up perspective of theresources and/or using macroscopic models that represent the top-downperspective of the organization. To be described in more detail below,the model 170 and various forms of resource data among the resourcedatabase 126 (including data to forecast the demand for differentresources) may be used to facilitate an analysis and optimization ofresource dynamics as part of a data-driven decision making underuncertainty framework. An example below describes the development of themodel 170 using macroscopic models. The model 170 may model resourcedynamics such as the effects of an evolving composition of resourcesover time on the entity 130. The model 170 may provide an estimate of atrajectory of the resource compositions under current or alternativerules and policies defined by the entity 130.

In the descriptions below, a bold-type notation may represent vectorsand matrices, with their elements identified through subscripts,blackboard bold may represent sets of elements, the straight brackets || may represent a number of elements in a set or an operator (e.g., |A|denotes the number of elements in the set A), and a transpose of avector or a matrix is denoted by.

To develop the model 170 that models evolution and resource dynamics ofthe entity 130, the system 100 may train the model 170 that is based ona time inhomogeneous Markov chain denoted as Z(t):=(Z_(ω)

over the state space

:={n∈

:n≤N}, where

:=

×

×

represents the set of all possible combinations of the tier, function,and productivity attributes. The notation

is related to the function attribute, and denotes a family of subsets ofa set of functions, denoted as

, that may possibly be possessed by the resources of the entity 130(e.g., F is a subset of functions among a set of functions S). Thenotation

is related to the productivity attribute, and denotes a set ofproductivity levels of the resources of the entity 130, where a discretevalue q (q∈

) represents the fraction of work that the resource can accrue ascompared to an empirically estimated maximum amount that a resource mayexecute in a given time period. The notation

is related to the tier attribute, and denotes a set of possible tiers,such as

={1, 2, . . . , L}, of a resource within the entity 130 at any giventime, where a tier at a given time is represented by a positive integer

as an element of

, for integer L>0. The notations n=

and N=

, where N_(ω)<∞, define an upper bound on the number of resources thatmay possess the combination of attributes ω∈

. The random variable Z_(ω)(t) with ω=(f,

, q) represents the number of resource in a period tin which a resourcemay possess the family of skills f∈

, tier

∈

and productivity level q∈

. In an example, the entity 130 may define a hierarchical structure oftiers, such that the tier for a resource may be a first-order indicationof various attributes, such as competency, capabilities, experience,compensation level, cost, and/or other attributes possessed by theresource as well as the responsibilities assigned to the resource.

The processor 120 may perform an analysis on the time inhomogeneousMarkov chain over a horizon, or time range, of T+1 periods to analyze anevolution and resource dynamics of the entity 130 over time. In anexample, the analysis may begin at t=0 such that Z(0) provides aninitial number of resources for all probable combinations of families ofskills f∈

, tiers

∈

and productivity levels q∈

. Thus, the dynamics of the time inhomogeneous Markov chain mayconstitute the resource dynamics of the entity 130, such that the model170 may be denoted as Z(t+1) and may be expressed as:Z(t+1)=Z(t)+H(t)−A(t)+G′(t)1−G(t)1,t∈{0, . . . ,T}where H(t) denotes a |

|-dimensional random vector of the number of new resources added to theworkforce of the entity 130 for a time period t (e.g., new hiring), A(t)denotes a |

|-dimensional random vector of the number of current resources departingfrom the workforce of the entity 130 for a period t, G(t) denotes a |

|×|

| random matrix of a number of internal transitions by current resources(e.g., switch of positions or tiers within the entity 130) for a periodt, and 1 denotes a |

|-dimensional column vector of all ones. The random matrices G(t) mayincorporate all internal transitions from one time period to the next,including changes to functions, tier, and productivity. Further, thenotation G′(t)1 represents a random vector of the number of transitionsinto every ω∈

, whereas the notation G(t)1 represents a random vector of the number oftransitions out of every ω∈

. Also, G(t) may be time inhomogeneous random matrices, and H(t) andA(t) may be time inhomogeneous random vectors supporting different formsof time-varying behaviors for the evolutionary resource dynamics withinthe entity 130, including various types of seasonal effects exhibited inpractices by the entity 130.

In an example, a performance metric of the entity 130, such as anexpected net-benefit, based on the resource dynamics may be obtained asa difference between a set of rewards accrued and an amount of costsincurred by the entity 130 for resources with similar attributes. Forexample, let

(t) denote a reward rate of a maximally productive (or full capacity)resource possessing attributes (f,

), let

(t) denote the cost rate of the resource possessing the attributes (f,

), let

(t) denote the cost rate for adding a new resource possessing theattributes (f,

), and let

(t) denote a cost rate for training and related decisions applied to aresource possessing the attributes (f,

) to influence a transition from (f,

) to ({tilde over (f)},

) (e.g., transition from a novice to an expert level of a function), forperiod t. Then, for one or more resources of the entity 130 thatpossesses the attributes (f,

) over period t, the expected rewards

(t) and costs, including running costs

(t), training costs

(t) and additions (e.g., additions of resources) costs

(t), may become controls (e.g., controls 172) to the model 170, and maybe expressed as:

${\mathcal{R}_{({f,\ell})}(t)} = {{{\overset{\bigvee}{r}}_{({f,\ell})}(t)} \times {{\mathbb{E}}\lbrack {( {{Z_{({f,\ell,q})}(t)} \times q} )\bigwedge{D_{({f,\ell})}(t)}} \rbrack}}$${\mathcal{C}_{({f,\ell})}(t)} = {{{\overset{\bigvee}{c}}_{({f,\ell})}(t)} \times {{\mathbb{E}}\lbrack {Z_{({f,\ell,q})}(t)} \rbrack}}$${\mathcal{G}_{{({f,\ell})},{({\overset{\_}{f},\overset{\_}{\ell}})}}(t)} = {{{\overset{\bigvee}{g}}_{{({f,l})},{({\overset{\_}{f},\overset{\_}{\ell}})}}(t)} \times {{\mathbb{E}}\lbrack {G_{{({f,\ell,q})},{({\overset{\_}{f},\overset{\_}{\ell},\overset{\_}{q}})}}(t)} \rbrack}}$${\mathcal{H}_{({f,\ell})}(t)} = {{{\overset{\bigvee}{h}}_{({f,\ell})}(t)} \times {{\mathbb{E}}\lbrack {H_{({f,\ell,q})}(t)} \rbrack}}$where

(t) denotes a random variable of a demand for resources with theattributes (f,

) over period t and x∧y:=min{x, y}. Note that the rewards for any (f,

) may only be accrued up to the minimum of the total capacity and thetotal demand for such individuals over the period and that the variouscosts for any (f,

) do not depend on the productivity level of individuals. The totalcapacity for any (f,

) may be referred to as full-time equivalents (FTEs), and may representa cumulative ability to satisfy demand for resources with attributes (f,

) normalized by the maximally productive resources with these attributes(e.g., the number of resources possessing attributes (f,

, q) times the productivity for individuals at level q summed over allq). As will be described in more detail below, the processor 120 mayappend particular conditions (constraints and objectives) relating tothe controls 172 on the model 170 to generate the controlled model 174.

In some examples, the entity 130 may be a large-scale system, such asincluding a relatively large number of resources, and the resourcedatabase 126 may include a relatively large amount of data. In someexamples, the instructions 124 may indicate a threshold, such that theprocessor 120 may monitor a size (e.g., amount of data) of the resourcedatabase 126 based on the threshold. For example, in response to thesize of the resource database 126 being greater than the threshold, theprocessor 120 may determine a need to apply an approximation techniqueto train or develop the model 170. The processor 120 may execute theapproximation technique to determine solutions for the timeinhomogeneous Markov chain model 170 and the corresponding stochasticcontrols. In some examples, the approximation technique being applied bythe processor 120 may be a mean-field analysis through a fluid limit ofthe controlled Markov chain model 174, thus reducing the formulatedproblem to a linear optimization problem that can be solved by theprocessor 120. Further, accuracy for large-scale system may be assuredby the related functional strong law of large number for large-scalesystems. The processor 120 may also quantify uncertainties at atractable granularity by augmenting the mean-field approximation with aGaussian random variable that captures the variability and risksassociated with each action of the discrete-time dynamical system aspart of our mean field analysis. Then, the processor 120 may determine aset of solutions for the problem formulated from the approximated modelfor large-scale systems. The approximation technique may be a mean-fieldlimit approximation that significantly reduces the dimensionality of thestate space

while retaining key aspects of the resource dynamics. The application ofmean-field approximation may allow the processor 120 to determine adeterministic, discrete-time dynamical system, or a mean-field (or fluidlimit) model. Hence, the mean-field approximations may provide relativeaccurate results for entities that may be large-scale systems.

The application of the approximation technique may include using astandard fluid limit of the time inhomogeneous Markov chain Z(t), andfrom the functional strong law of large numbers, a deterministicdynamical system z(t)=(z_(ω)

with initial condition z(0) and system dynamics denoted as:z(t+1)=z(t)+h(t)−a(t)+g′(t)1−g(t)1,t∈{0, . . . ,T}where h(t):=(h_(ω)

, a(t):=(a_(ω)

, and g(t):=(g_(ω,{tilde over (ω)})

with h_(ω)(t) representing the expected number of new resourcespossessing attributes w that are added to the entity 130 in period t,a_(ω)(t) represents the expected number of current resources possessingattributes w who departed from the entity 130 in the period t,g_(ω,{tilde over (ω)})(t) represents the expected number of currentresources possessing attributes w that transitioned to possess theattributes {tilde over (ω)} in period t, and z_(ω)(t) represents theexpected number of resources of the entity 130 that possess attributes ωfor time t. Thus, the approximated model z(t+1) may model the resourcedynamics of the entity 130 over the state space

:={{circumflex over (n)}∈

:{circumflex over (n)}≤N}. In some examples, z_(ω)(t) may be anapproximation of

[Z_(ω)(t)], and z_(ω)(t) may take on fractional values, unlike therandom variable Z_(ω)(t), corresponding to the population possessing theattributes ω∈B for time t.

Based on the approximated model (approximation of the model 170), theprocessor 120 may also approximate the expected net-benefit of theresource dynamics, such as a difference between the rewards accrued andthe costs incurred by the entity 130. For example, under the mean-fieldapproximation model, for resources the organization possessingattributes (f,

) over the period t, the approximated expected rewards

(t), running costs

(t), hiring costs

(t) and training costs

(t) may be used to control the approximated model, and may be denotedas:

${{\hat{\mathcal{R}}}_{({f,\ell})}(t)} = {{{\overset{\bigvee}{r}}_{({f,\ell})}(t)} \times {{{\mathbb{E}}\lbrack {{Z_{({f,\ell,q})}(t)} \times q} )}\bigwedge{{\mathbb{E}}\lbrack {D_{({f,\ell})}(t)} \rbrack}}}$${{\hat{\mathcal{C}}}_{({f,\ell})}(t)} = {{{\overset{\bigvee}{c}}_{({f,\ell})}(t)} \times {{\mathbb{E}}\lbrack {Z_{({f,\ell,q})}(t)} \rbrack}}$${{\hat{\mathcal{H}}}_{({f,\ell})}(t)} = {{{\overset{\bigvee}{h}}_{({f,\ell})}(t)} \times {{\mathbb{E}}\lbrack {H_{({f,\ell,q})}(t)} \rbrack}}$${{\hat{\mathcal{G}}}_{{({f,\ell})},{({\overset{\_}{f},\overset{\_}{\ell}})}}(t)} = {{{\overset{\bigvee}{g}}_{{({f,l})},{({\overset{\_}{f},\overset{\_}{\ell}})}}(t)} \times {{\mathbb{E}}\lbrack {G_{{({f,\ell,q})},{({\overset{\_}{f},\overset{\_}{\ell},\overset{\_}{q}})}}(t)} \rbrack}}$

The processor 120 may perform various evaluations of a performance ofthe entity 130 by formulating various optimal control problemsassociated with the model 170 of resource dynamics. The formulation ofthe optimal control problems may provide indications of how thecompositions of resources evolve over time under particular decisionstaken as part of a new control policy. For example, the processor 120may solve the optimal control problems (e.g., determine a set ofsolutions to the model 170 under particular objectives and constraints)to identify an optimal set of policies and assessment to be taken overtime to influence future resource dynamics in desired directions alongwith estimates of the trajectory of the resource composition under thecorresponding control policy.

In an example, the controls 172 may indicate an objective to identify aset of resources that may maximize an expected net-benefit of the entity130 over a given planning horizon (e.g., a set of future times),including various costs for resource decisions such as hiring,promoting, training and incentivizing retention. The processor 120 mayformulate an optical control problem by using the received controls 172indicating objectives and constraints to integrate controls on one ormore characteristics of the model 170, resulting in the controlled model174. The processor 120 may determine the outcomes 175 by solving thecontrolled model 174. In some examples, the objectives and constraintsindicated by the controls 172 may represent various resource decisionsand assessments available to the entity 130 at any time period, such ascomposing a group of resources that will maximize a performance metricof the entity 130.

A state space of the controlled model 174 may be the same as thetime-inhomogeneous Markov chain model 170 described above, which is

:={n∈

:n≤N}, where

:=

×

×

represents the set of all possible combinations of the tier, function,and productivity attributes (f,

, q). Let H(t) be a |

|-dimensional vector that represents the number of new resources addedto the workforce of the entity 130 for every w at time t, and V(t)denotes a |

|×|

| matrix that represents a number of transitions from a current set ofresources for every w to the population for every other {tilde over (ω)}through any combination of skill or function acquisition, tierpromotion, or productivity adjustment at time t, where ω:=(f,

, q) and {tilde over (ω)}:=({tilde over (f)},

, {tilde over (q)}). Further, let M(t) be a |

|-dimensional vector that represents an amount of incentives that may beprovided to the resources improve retention. Let X(t):=(X_(ω)

represent a stochastic process governed by the controlled Markov chainor model 170 (e.g., the vector of the current set of resources for eachcombination of attributes (f,

, q)∈

at time t). Thus, the resource dynamics corresponding to the model 170with the addition of controls H(t), V(t) and M(t) may become thecontrolled model 174, which may be denoted as X(t+1) and expressed as:X(t+1)=X(t)+H(t)+(V′(t)−V(t))1+(W′ ^((t)) −W(t))1−A _(M)(t)where 1 denotes a |

|-dimensional vector whose components are all 1, A_(M)(t) denotes arandom |

|-vector representing an amount of lost resources at time t given thatM_(ω)(t) amount of incentives are provided to each ω∈

, and W(t) represents a random matrix representing the transitionsbetween different sets of resources which may be irrelevant to theentity 130. Note that the probability laws governing A_(M)(t) and W(t)depend on the state X(t) and controls H(t), V(t) and M(t). In someexamples, the processor 120 may determine functional of the Markov chainwith the addition of costs for any resource decisions indicated by thecontrolled model 174, where the functionals may represent a net-benefitof the entity 130. In some examples, the objectives may be defined withrespect to different resource decisions and assessments, which may leadto the net-benefit functionals and corresponding optimal control problemformulation being based on particular sets of resources among the entity130.

In some examples, the processor 120 may formulate the control problemsbased on resource decisions pertaining to a specific area (e.g.,finance, risk management, and/or other area) of the entity 130. Forexample, a particular formulation of the controlled model 174 may bebased on current resources with specific combinations of skills f∈

, without allowing transitions between combinations of skills from f∈

to {tilde over (f)}∈

(e.g., keeping the function attribute fixed). As a result, thecombination of functions possessed by resources may be limited, and theresource decisions of adding resources at each level (

, q)∈

×

, training, and related decisions to influence transitions betweenlevels from (

, q)∈

×

to (

, {tilde over (q)})∈

×

may also be limited. In such an example, the net-benefit functionals mayinclude expected revenue, expected maintenance cost and expectedworkforce-decision costs, at the state-space level of (

, q), and may be represented as:

(t)=

(t)×

(t)∧

(t)]

(t)=

(t)×

(t)]

(t)=

(t)×

(t)]

(t)=

(t)×

(t)]

(t)=

(t)×

(t)]where

(t) and

(t) respectively denote the reward and cost rate of a resourcepossessing attributes (

, q),

(t) denotes a random variable of the demand for resources possessingattributes (

, q),

(t) denotes a cost rate of adding a resource possessing attributes (

, q),

(t) denotes the cost rate of incentivization decisions, and

(t) denotes the cost rate of training and related decisions applied to aresource possessing attributes (

, q) to influence resources transitions to (

, {tilde over (q)}), all for period t. Thus, the corresponding optimalcontrol problem represented by controlled model 174 may be representedas:

$\sum\limits_{y = 1}^{T}{( {{\mathcal{R}_{({\ell,q})}(t)} - {\mathcal{C}_{({\ell,q})}(t)} - {\mathcal{H}_{({\ell,q})}(t)} - {\mathcal{M}_{({\ell,q})}(t)} - {\mathcal{G}_{{({\ell,q})},{({\overset{\_}{\ell},\overset{\_}{q}})}}(t)}} )}$

In another example, the controls 172 may include constraints andobjectives relating to a certain degree of aggregation needed forvarious financial planning for the entity 130, such as the need toreduce the complexity of the enterprise operations of the entity 130 andto forecast future demand at a optimal (e.g., most) accurate level(e.g., forecasting may not always be the most accurate at the lowestgranularities of resource attributes). For this example, the demandforecast will be produced for any combination of family of skills f andposition level

, not distinguishing productivity levels (e.g. keeping q fixed). Hence,the net-benefit functionals will be expressed at the state-space levelof (f,

), and

(t)=

(t)×

(t)], which includes the consideration of addition of extra costs forany resource decisions and assessment. Thus, the corresponding optimalcontrol problem represented by controlled model 174 may be representedas:

$\sum\limits_{y = 1}^{T}{\sum\limits_{{({f,\ell})} \in {{\mathbb{F}} \times {\mathbb{L}}}}( {{\mathcal{R}_{({f,\ell})}(t)} - {\mathcal{C}_{({f,\ell})}(t)} - {\mathcal{H}_{({f,\ell})}(t)} - {\mathcal{M}_{({f,\ell})}(t)} - {\sum\limits_{\underset{{({\overset{\_}{f},\overset{\_}{\ell}})} \neq {({f,\ell})}}{{({\overset{\_}{f},\overset{\_}{\ell}})} \in {{\mathbb{F}} \times {\mathbb{L}}}}}{\mathcal{G}_{{({f,\ell})},{({\overset{\_}{f},\overset{\_}{\ell}})}}(t)}}} )}$  where${K( {t,{X(t)},{H(t)},{V(t)},{M(t)}} )} = {\sum\limits_{{({f,\ell})} \in {{\mathbb{F}} \times {\mathbb{L}}}}( {{\mathcal{R}_{({f,\ell})}(t)} - {\mathcal{C}_{({f,\ell})}(t)} - {\mathcal{H}_{({f,\ell})}(t)} - {\mathcal{M}_{({f,\ell})}(t)} - {\sum\limits_{\underset{{({\overset{\_}{f},\overset{\_}{\ell}})} \neq {({f,\ell})}}{{({\overset{\_}{f},\overset{\_}{\ell}})} \in {{\mathbb{F}} \times {\mathbb{L}}}}}{\mathcal{G}_{{({f,\ell})},{({\overset{\_}{f},\overset{\_}{\ell}})}}(t)}}} )}$

The processor 120 may further derive one or more structural propertiesfor the optimal control problems presented above (for fixed f, and fixedq). In an example, a stochastic dynamic program may be formulated andsolved by the processor 120 to derive the structural properties of thecontrol problems. For example, a stochastic dynamic program may bedefined with a value function, denoted as J(t, X(t)), that represents avalue function for maximizing an expected net-benefit of the optimalcontrol problem over the time horizon from t to T. The stochasticdynamic program, which may be solved by the processor 120, may beexpressed as:J(t,X(t))=max P(t,X(t),H(t),V(t),M(t))P(t,X(t),H(t),V(t),M(t))=K(t,X(t),H(t),V(t),M(t))+

[J(t+1,X(t+1))]where this stochastic dynamic program may be used to obtain one or morestructural properties. In an example, a structural property may be, forany time t, there exists a finite control vector that optimizes thestochastic dynamic program, whose solution renders the optimal controlpolicy for the control problem of the controlled model 174 relating tothe fixed q. The structural property allows the processor 120 toefficiently compute an optimal solution of the controlled model 174relating to the fixed q through convex programming, particularly forsmall to moderate size control problems. However, in examples where thesize of the state space

increases to large-scale systems, the optimal control of the fluid limitof the controlled model 174 may be considered.

A state space of the approximated controlled model (of controlled model174) may be

:={n∈

:n≤N}, where

:=

×

×

represents the set of all possible combinations of the tier, function,and productivity attributes (f,

, q). In an example, the controls 172 being applied on the mean-fieldapproximated controlled model 174 may represent the resource decisionsavailable to the entity at any time period. Let h(t) be a |

|-dimensional vector that represents the expected number of additions tothe population of resource for every ω through resource additions attime t, and v(t) denotes a |

|×|

| matrix that represents the expected number of transitions from thepopulation for every ω to the population for every other {tilde over(ω)} through any combination of skill acquisition, level promotion, orproductivity adjustment at time t, where ω:=(f,

, q) and {tilde over (ω)}:=({tilde over (f)},

, {tilde over (q)}). Further, let m(t) be a |

|-dimensional vector that represents the amount of incentives(financially and organizationally) provided to improve expectedretention, thus reducing expected amount of lost resources. Letx(t):=(x_(ω)

to be the deterministic dynamical system governed by the mean fieldcontrol process (e.g., the vector of the expected population for eachcombination of attributes (f,

, q)∈

at time t). Thus, the resource dynamics corresponding to the controlledmodel 174 with the addition of controls h(t), v(t) and m(t) may becomethe approximated controlled model 174 denoted as x(t+1) and expressedas:x(t+1)=x(t)+h(t)+(v′(t)−v(t))1+(w′ ^((t)) −w(t))1−a _(M)(t)where 1 denotes a |

|-dimensional vector whose components are all 1, a_(M)(t) denotes a |

|-dimensional vector representing the expected amount of lost resourcesat time t given that m_(ω)(t) amount of incentives are provided to eachω∈

, and w(t) denotes a |

|×|

| matrix representing the expected transitions between differentresources which are not managed by the entity 130. Note that theprobability laws governing a_(M)(t) and w(t) depend on the state x(t)and controls h(t), v(t) and m(t).

Using the above approximation of controlled model 174, a control problemof the controlled model 174 with fixed q may be formulated with

(t)=

(t)×

(t), which include the addition of extra costs for any resourcedecisions taken. The control problem of the controlled model 174 withfixed q may be represented as:

$\sum\limits_{y = 1}^{T}{\sum\limits_{{({f,\ell})} \in {{\mathbb{F}} \times {\mathbb{L}}}}( {{{\overset{\bigvee}{\mathcal{R}}}_{({f,\ell})}(t)} - {{\overset{\bigvee}{\mathcal{C}}}_{({f,\ell})}(t)} - {{\overset{\bigvee}{\mathcal{H}}}_{({f,\ell})}(t)} - {{\overset{\bigvee}{\mathcal{M}}}_{({f,\ell})}(t)} - {\sum\limits_{\underset{{({\overset{\_}{f},\overset{\_}{\ell}})} \neq {({f,\ell})}}{{({\overset{\_}{f},\overset{\_}{\ell}})} \in {{\mathbb{F}} \times {\mathbb{L}}}}}{{\overset{\bigvee}{\mathcal{G}}}_{{({f,\ell})},{({\overset{\_}{f},\overset{\_}{\ell}})}}(t)}}} )}$In order to solve this deterministic optimal control problem, thefollowing standard discrete-time linear dynamical system is used:x(t+1)=A(t)y(t)+B(t)u(t)where A(t)=[

], y′(t)=[x(t), (w′(t)−w(t))1], B(t)=[{tilde over (B)}(t),

, −

], and u(t)=[{tilde over (v)}′(t), h(t), a_(m)(t)]. {tilde over (B)}(t)may denote a |

|×|

| matrix whose element equals 1 when k≠i and {tilde over (k)}=i, andequals −1 when k=i and {tilde over (k)}≠i, and equals 0 when k={tildeover (k)}=i. {tilde over (v)}′(t) denotes the vectorization of a matrixv(t), and I_(k) denotes a k×k identity matrix. The notations x(t) andu(t) may be the state and decision vectors of the linear dynamicalsystem at time t, respectively. The processor 120 may determine outcomes175, which in this example is an appropriate set of decision vectorsu(0), . . . , u(T−1) that may optimize a performance over the timehorizon T. The objective function, which may be indicated by thecontrols 172, may be related to identifying a global maximum denoted as:

$\max{\sum\limits_{t = 0}^{T - 1}\lbrack {{{p( {t + 1} )} \cdot {x( {t + 1} )}} - {{d(t)} \cdot {u(t)}}} \rbrack}$where p(t) represents, in vector form, state profits as a differencebetween

(t) and

(t) at time t, and d(t) represents, in vector form, resource decisioncosts as the sum of

(t),

(t), and

(t) at time t. The processor 120 may formulate and solve a linearprogram denoted as:max ψ·ϕs.t.ψ′=[x(1) . . . x(T)u(0) . . . u(T−1)]≥0,ϕ′=[p(1) . . . p(T)−d(0) . . . −d(T−1)],x(t+1)=A(t)y(t)+B(t)u(t)t=0, . . . ,T−1,  (a)0≤(v(t)+w(t))1≤x(t)t=0, . . . ,T−1,  (b)0≤a _(m)(t)≤a(t)t=0, . . . ,T−1,  (c)where both the initial state vector x(0) and the weight vector ϕ areprovided to the processor 120 as inputs, and the constraints (a), (b),(c) are all component-wise. These constraints ensure that, for all t,the state and decision vectors are nonnegative, the flow of resourcesout of each state are nonnegative and do not exceed the number ofresources in the state, and the reduction in the amount of lostresources due to the incentivizing retention does not exceed theoriginal amount of lost resources. The solution of this linear programprovides an optimal vector ψ* from which the relevant informationregarding optimal state vectors x*(t) for t=1, . . . , T, and optimaldecision vectors h*(t), v*(t) and m*(t) for t=0, . . . , T−1, may beselected. In examples for large-scale systems, the above linear programmay be solved with optimization software. In some examples, when thesize of the linear program becomes significantly large that thecomputation time by the processor 120 exceeds a desired response time,then subset of combinations of attributes (f,

, q) may be considered, instead of all the possible combinations ofattributes (f,

, q). In an example, the processor 120 may extract data relating toresources that possess unions of particular elements in

,

, and

, to obtain aggregated sets of the family of skills

, the tier levels

and the productivity levels

, resulting in a reduced set of

:=

×

×

such that the processor 120 may solve the above linear program using thereduced set

.

In some examples, the system 100 may further consider transitioneligibility of the resources among the entity 130. For example, after aparticular amount of time, a first tier resource may be eligible to bepromoted to a second tier, where the promotion may cause an increase incost incurred on the entity 130. The promotion, in some examples, mayalso trigger transitions relating to other resources. For example, afterpromoting a tier of a resource, a new resource may be assigned toperform the tasks previously assigned to the promoted resource. Further,a requirement for particular productivity demands may also change inresponse to a promotion of tier (e.g., an increase in an expectedefficiency from a resource of a higher tier). Thus, the model 170 andcontrolled model 174 generated by the system 100 may account for variousforecast of transitions based on known transition eligibility defined bythe entity 130.

In some examples, the outcomes 175 may be stored in the memory 122 suchthat the processor 120 may use the outcomes 175 to retrain the model 170autonomously using updated outcomes 175. Further, the processor 120 mayupdate the resource database 126 periodically or in response totransitions of resources, such as addition, tier promotion, and/or othertransitions, and may use the updated data in the resource database 126to retrain the model 170 autonomously.

Further, in examples where the resources are part of the workforce ofthe entity 130, the outcomes 175 may be provided to the resource suchthat the resource may review the model 170 indicating a forecast oftheir career trajectory. Therefore, each individual among the workforcemay be aware of any potential needs, such as training or enhancement,that may help them improve their career trajectory.

FIG. 2 illustrates a flow diagram relating to a process, in oneembodiment, to implement performance evaluation based on resourcedynamics, arranged in accordance with at least some embodimentspresented herein. The process in FIG. 2 may be implemented using, forexample, computer system 100 discussed above. An example process mayinclude one or more operations, actions, or functions as illustrated byone or more of blocks 202, 204, 206, 208, 210, 212, and/or 214. Althoughillustrated as discrete blocks, various blocks may be divided intoadditional blocks, combined into fewer blocks, eliminated, or performedin parallel, depending on the desired implementation.

Processing may begin at block 202, where a processor obtains first dataindicating tier attributes of a plurality of resources of an entity. Thefirst data includes a quantified tier of the tier attributes of theplurality of resources. Processing may continue from block 202 to block204. At block 204, the processor obtains second data indicating functionattributes of the plurality of resources. The second data includes acompetency level of each function among the function attributespossessed by each resource among the plurality of resources. Processingmay continue from block 204 to block 206. At block 206, the processorobtains third data indicating productivity attributes of the pluralityof resources. The productivity attributes are based on historical datarelating to performance of the plurality of resources.

Processing may continue from block 206 to block 208. At block 208, theprocessor trains a model based on the first data, the second data, andthe third data. The model represents one or more transitions of theplurality of resources over time. The transitions of the plurality ofresources are based on the tier attribute, the function attribute, andthe productivity attribute of the plurality of resources. In someexamples, the model is based on a time inhomogeneous Markov chain.

Processing may continue from block 208 to block 210. At block 210, theprocessor receives a set of controls comprising at least an objective tooptimize a performance of the entity. Processing may continue from block210 to block 212. At block 212, the processor generates a controlledmodel by integrating the set of controls into the model. Processing maycontinue from block 212 to block 214. At block 214, the processordetermines a set of outcomes from the controlled model. The set ofoutcomes includes at least a set of transitions relating to theplurality of resources, and the set of transitions optimizes theperformance of the entity.

In some examples, the set of outcomes may include recommendations to addat least one resource of a particular attribute to the plurality ofresources, where the particular attribute comprising at least one of thetier attribute, the function attribute, and the productivity attribute.The set of outcomes may further include recommendations to modify atleast one composition of resources in the entity. The set of outcomesmay further include recommendations to assign different tasks to atleast one composition of the resources in the entity. In some examples,the processor may further determine an amount of resources of theplurality of resources exceeds a threshold, and apply an approximationtechnique on the model to determine the set of outcomes. The processormay further include retraining the model using the set of outcomes.

FIG. 3 illustrates a schematic of an example computer or processingsystem that may implement performance evaluation based on resourcedynamics in one embodiment of the present disclosure. The computersystem is only one example of a suitable processing system and is notintended to suggest any limitation as to the scope of use orfunctionality of embodiments of the methodology described herein. Theprocessing system shown may be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with the processingsystem shown in FIG. 3 may include, but are not limited to, personalcomputer systems, server computer systems, thin clients, thick clients,handheld or laptop devices, multiprocessor systems, microprocessor-basedsystems, set top boxes, programmable consumer electronics, network PCs,minicomputer systems, mainframe computer systems, supercomputers, anddistributed cloud computing environments that include any of the abovesystems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a module 30 (e.g.,resource dynamics module 30) that performs the methods described herein.The module 30 may be programmed into the integrated circuits of theprocessor 12, or loaded from memory 16, storage device 18, or network 24or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

FIG. 4 depicts a cloud computing environment according to an embodimentof the present invention. It is to be understood that although thisdisclosure includes a detailed description on cloud computing,implementation of the teachings recited herein are not limited to acloud computing environment. Rather, embodiments of the presentinvention are capable of being implemented in conjunction with any othertype of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 4, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 4 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

FIG. 5 depicts abstraction model layers according to an embodiment ofthe present invention. Referring now to FIG. 125 a set of functionalabstraction layers provided by cloud computing environment 50 (FIG. 4)is shown. It should be understood in advance that the components,layers, and functions shown in FIG. 5 are intended to be illustrativeonly and embodiments of the invention are not limited thereto. Asdepicted, the following layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and resource dynamics modeling 96.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A computer-implemented method comprising:obtaining, by a processor, first data indicating tier attributes of aplurality of resources of an entity; obtaining, by the processor, seconddata indicating function attributes of the plurality of resources;obtaining, by the processor, third data indicating productivityattributes of the plurality of resources; quantifying, by the processor,the first data, the second data, and the third data such that the tierattributes, the function attributes, and the productivity attributes arerepresented in a format processible by the processor to train a machinelearning model; training, by the processor, a model based on thequantified first data, the quantified second data; and the quantifiedthird data, wherein the trained model is developed to characterizeevolutions of the plurality of resources over time, the evolutions ofthe plurality of resources includes one or more transitions of theplurality of resources over time, and the transitions of the pluralityof resources are based on the tier attribute, the function attribute,and the productivity attribute of the plurality of resources; receiving,by the processor, a set of controls comprising at least an objective tooptimize a performance of the entity; generating, by the processor, acontrolled model by integrating the set of controls into the model; anddetermining, by the processor, a set of outcomes from the controlledmodel, wherein the set of outcomes comprises at least a set oftransitions relating to the plurality of resources, and the set oftransitions optimizes the performance of the entity.
 2. Thecomputer-implemented method of claim 1, wherein the model is based on atime inhomogeneous Markov chain.
 3. The computer-implemented method ofclaim 1, wherein the second data comprises a competency level of eachfunction among the function attributes possessed by each resource amongthe plurality of resources.
 4. The computer-implemented method of claim1, wherein the productivity attributes are based on historical datarelating to performance of the plurality of resources.
 5. Thecomputer-implemented method of claim 1, wherein the set of outcomesindicate at least one of: a recommendation to add at least one resourceof a particular attribute to the plurality of resources, the particularattribute comprising at least one of the tier attribute, the functionattribute, and the productivity attribute; a recommendation to modify atleast one composition of resources in the entity; and a recommendationto assign different tasks to at least one composition of the resourcesin the entity.
 6. The computer-implemented method of claim 1, furthercomprising: determining an amount of resources of the plurality ofresources exceeds a threshold; and applying an approximation techniqueon the model to determine the set of outcomes.
 7. Thecomputer-implemented method of claim 1, further comprising retrainingthe model using the set of outcomes.
 8. A system comprising: a memorydevice configured to store a database that comprises: first dataindicating tier attributes of a plurality of resources of an entity;second data indicating function attributes of the plurality ofresources; third data indicating productivity attributes of theplurality of resources; a hardware processor configured to be incommunication with the memory device, the hardware processor beingconfigured to: obtain the first data, the second data, and the thirddata from the memory device; quantify the first data, the second data,and the third data such that the tier attributes, the functionattributes, and the productivity attributes are represented in a formatprocessible by the processor to train a machine learning model; train amodel based on the quantified first data, the quantified second data,and the quantified third data, wherein the trained model is developed tocharacterize evolutions of the plurality of resources over time, theevolutions of the plurality of resources includes one or moretransitions of the plurality of resources over time, and the transitionsof the plurality of resources are based on the tier attribute, thefunction attribute, and the productivity attribute of the plurality ofresources; receive a set of controls comprising at least an objective tooptimize a performance of the entity; generate a controlled model byintegrating the set of controls into the model; and determine a set ofoutcomes from the controlled model, wherein the set of outcomescomprises at least a set of transitions relating to the plurality ofresources, and the set of transitions optimizes the performance of theentity.
 9. The system of claim 8, wherein the model is based on a timeinhomogeneous Markov chain.
 10. The system of claim 9, wherein: thesecond data comprises a competency level of each function among thefunction attributes possessed by each resource among the plurality ofresources; and the productivity attributes are based on historical datarelating to performance of the plurality of resources.
 11. The system ofclaim 8, wherein the set of outcomes indicate at least one of: arecommendation to add at least one resource of a particular attribute tothe plurality of resources, the particular attribute comprising at leastone of the tier attribute; the function attribute, and the productivityattribute; a recommendation to modify at least one composition ofresources in the entity; and a recommendation to assign different tasksto at least one composition of the resources in the entity.
 12. Thesystem of claim 8, wherein the hardware processor is further configuredto: determine an amount of resources of the plurality of resourcesexceeds a threshold; and apply an approximation technique on the modelto determine the set of outcomes.
 13. The system of claim 8, wherein thehardware processor is further configured to retrain the model using theset of outcomes.
 14. A computer program product of evaluating aperformance of an entity, the computer program product comprising anon-transitory computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processing element of a device to cause the device to: obtain firstdata indicating tier attributes of a plurality of resources of anentity; obtain second data indicating function attributes of theplurality of resources; obtain third data indicating productivityattributes of the plurality of resources; quantify the first data, thesecond data, and the third data such that the tier attributes, thefunction attributes, and the productivity attributes are represented ina format processible by the processor to train a machine learning model;train a model based on the quantified first data, the quantified seconddata, and the quantified third data, wherein the trained model isdeveloped to characterize evolutions of the plurality of resources overtime, the evolutions of the plurality of resources includes one or moretransitions of the plurality of resources over time, and the transitionsof the plurality of resources are based on the tier attribute, thefunction attribute, and the productivity attribute of the plurality ofresources; receive a set of controls comprising at least an objective tooptimize a performance of the entity; generate a controlled model byintegrating the set of controls into the model; and determine a set ofoutcomes from the controlled model, wherein the set of outcomescomprises at least a set of transitions relating to the plurality ofresources, and the set of transitions optimizes the performance of theentity.
 15. The computer program product of claim 14, wherein the modelis based on a time inhomogeneous Markov chain.
 16. The computer programproduct of claim 14, wherein: the second data comprises a competencylevel of each function among the function attributes possessed by eachresource among the plurality of resources; and the productivityattributes are based on historical data relating to performance of theplurality of resources.
 17. The computer program product of claim 14,wherein the set of outcomes indicate at least one of: a recommendationto add at least one resource of a particular attribute to the pluralityof resources, the particular attribute comprising at least one of thetier attribute, the function attribute, and the productivity attribute;a recommendation to modify at least one composition of resources in theentity; and a recommendation to assign different tasks to at least onecomposition of the resources in the entity.
 18. The computer programproduct of claim 14, wherein the program instructions are furtherexecutable by the processing element of the device to cause the deviceto: determine an amount of resources of the plurality of resourcesexceeds a threshold; and apply an approximation technique on the modelto determine the set of outcomes.
 19. The computer program product ofclaim 14, wherein the program instructions are further executable by theprocessing element of the device to cause the device to retrain themodel using the set of outcomes.